Machine Learning Assisted Exploration of High Entropy Alloy-Based Catalysts for Selective CO2 Reduction to Methanol,The Journal of Physical Chemistry Letters

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Machine Learning Assisted Exploration of High Entropy Alloy-Based Catalysts for Selective CO2 Reduction to Methanol
The Journal of Physical Chemistry Letters ( IF 4.8 ) Pub Date : 2022-06-23 , DOI: 10.1021/acs.jpclett.2c00929
Diptendu Roy ₁ , Shyama Charan Mandal ₁ , Biswarup Pathak ₁

Affiliation

Catalytic conversion of CO₂ to carbon neutral fuels can be ecofriendly and allow for economic replacement of fossil fuels. Here, we have investigated high-throughput screening of high entropy alloy (Cu, Co, Ni, Zn, and Sn) based catalysts through machine learning (ML) for CO₂ hydrogenation to methanol. Stability and catalytic activity studies of these catalysts have been performed for all possible combinations, where different elemental, compositional, and surface microstructural features were used as input parameters. Adsorption energy values of CO₂ reduction intermediates on the CuCoNiZnMg- and CuCoNiZnSn-based catalysts have been used to train the ML models. Successful prediction of adsorption energies of the adsorbates using CuCoNiZnMg-based training data is achieved except for two intermediates. Hence, we show that activity and selectivity of these catalysts can be successfully predicted for CO₂ hydrogenation to methanol and have screened a series of high entropy-based catalysts (from 36750 considered catalysts) which could be promising for methanol synthesis.

中文翻译：

机器学习辅助探索基于高熵合金的催化剂用于选择性地将二氧化碳还原为甲醇

_{CO 2}催化转化为碳中性燃料可以是生态友好的，并且可以经济地替代化石燃料。在这里，我们通过机器学习 (ML) 研究了高熵合金（Cu、Co、Ni、Zn 和 Sn）基催化剂的高通量筛选，用于 CO ₂加氢制甲醇。已经对所有可能的组合进行了这些催化剂的稳定性和催化活性研究，其中不同的元素、组成和表面微观结构特征被用作输入参数。_{CO 2}的吸附能值CuCoNiZnMg 和 CuCoNiZnSn 基催化剂上的还原中间体已用于训练 ML 模型。除了两个中间体之外，使用基于 CuCoNiZnMg 的训练数据成功预测了吸附物的吸附能。因此，我们表明这些催化剂的活性和选择性可以成功预测 CO ₂加氢制甲醇，并筛选了一系列高熵基催化剂（来自 36750 种被认为是催化剂），这些催化剂有望用于甲醇合成。

更新日期：2022-06-23

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